Emergency Call Manual Mapping Using Rules and AI Models
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Solution Overview
Problem
Existing emergency call response systems rely heavily on operator capabilities and conditions, leading to variable and potentially inadequate responses during disasters or incidents.
Innovation Solution
An apparatus and method that utilizes an input unit for voice calls, preprocessing, keyword detection, model generation, and manual mapping to provide a real-time response manual based on emergency call data, employing both rule-based and model-based mapping techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based mapping is used for manual mapping, then mapping accuracy can be maintained in early stages, but the system cannot adapt to new emergency call patterns without manual rule updates
Solution Approach 1:
The system transitions from static rule-based mapping to dynamic model-based mapping that automatically adapts to new emergency call patterns. The NLP model is trained on historical emergency call data and continuously improves its mapping accuracy without requiring manual rule updates, while maintaining the ability to handle novel situations through learned patterns.
Solution Approach 2:
The system uses pseudo-labeling to create synthetic training data by copying and adapting existing manual mappings. Unlabeled emergency call data is assigned pseudo-labels based on similarity to existing mapped data, creating a expanded training set that enables the model to learn from diverse patterns while maintaining consistency with established mapping standards.
2Adaptability or versatility
If model-based mapping is used, then the system can adapt to new emergency call patterns, but mapping accuracy decreases when training data is insufficient
Solution Approach 1:
The system performs preliminary manual mapping of representative emergency call examples to create initial training data before deploying the NLP model. This preliminary action establishes a foundation of accurately mapped examples that the model can learn from, ensuring initial mapping accuracy while enabling future adaptability as the model trains on this and additional data.
Solution Approach 2:
The system implements a feedback mechanism where mapping results are continuously evaluated and used to retrain the NLP model. When new emergency calls are mapped, their accuracy is assessed, and successful mappings are added to the training set, creating a continuous improvement loop that enhances both accuracy and adaptability over time.
3Measurement precision
If extensive manual mapping is performed, then mapping accuracy improves, but the time and resources required for manual work increase
Solution Approach 1:
The system enables self-service mapping through the NLP model that automatically maps emergency calls to appropriate manuals without requiring extensive manual intervention. The model learns from initially mapped examples and independently handles subsequent mapping tasks, dramatically reducing the time and resources needed for manual mapping while maintaining high accuracy through continuous learning.
Solution Approach 2:
The NLP model serves multiple functions: it maps new emergency calls, identifies patterns in existing data, generates pseudo-labels for unlabeled data, and provides recommendations for manual rule updates. This multi-functionality consolidates what would otherwise require separate manual processes into a single automated system, reducing overall time investment while improving consistency and accuracy.
Data Source
AI summary
According to an embodiment of the present disclosure, an apparatus for An apparatus for mapping an emergency call data manual, the apparatus comprising: an input unit that receives an emergency call in voice form; a preprocessing unit that converts the voice form into text data and preprocesses the text data to generate an emergency call data token; a keyword detection unit that detects main keywords using a manual and generates a mapping rule using the main keywords; a model generation unit that generates a model by training an artificial intelligence model using manual-mapped data; a manual mapping unit that generates a response manual for the text data by performing mapping based on the mapping rule and emergency call data token and mapping based on the model; and a display unit that displays the response manual.